Web search result clustering based on K-center and information gain
基于 K-center和信息增益的 Web搜索结果聚类方法 *

Based on K-center and informationgain, this paper represented aversion of modified FPF algorithman dclusterla-beling algorithm on Web search clustering, made the result better understood. At last, presented a simple and intuitionistic criterion NMI for estimating cluster quality. The proposed solution was evaluated in search results returned from actual Web search engine and compared with other methods, like Lingo, K-means. The result proves that the algorithm can balance better clustering time and quality, and meets the requirements of Web searching clustering.